The amount of content needed to create engaging digital experiences has easily doubled, maybe even tripled. Why? The payoff is big. 

More current and targeted content leads to improved digital experiences, resulting in deeper customer engagement and increased revenues. It’s easy to see why “content marketing revenues are projected to grow at a 14.4 percent compound annual growth rate from 2017 to 2021,” according to Dawn Papandrea of NewsCred Insights.

So how can marketers keep up with this insatiable demand for content? Many digital leaders are turning to the use of natural language generation (NLG), an artificial intelligence-based marketing tool, to scale content creation and ease some of the pressure on marketers by taking over routine content creation tasks. The market for content appears headed for growth as analyst firm Gartner estimates that “by 2020, natural-language generation and artificial intelligence will be a standard feature of 90 percent of modern BI platforms."

Natural Language Generation: The Benefits and Limitations 

NLG can help marketers in a variety of ways including:

  • Auto-generate editorial text without the need for additional human resources, helping marketing teams operate more efficiently.
  • Create content variations for unlimited personas for highly personalized digital experiences.
  • Reduce the time spent creating large quantities of content down to mere seconds, delivering economies of scale and faster time to value.

However, as with most forms of technology, NLG has its limitations.

Marketers must count on investing a significant amount of time when getting started with NLG. Be realistic when setting out to test this technology and give yourself the time to set it up properly. Metadata, keywords and other data sources needed to generate the content must be identified and structured. Without this step, NLG won’t be useful. In addition, NLG technology needs to learn a company's voice over time — this is the AI/machine learning aspect of NLG. This means there is ramp up time to reach that human-like voice.

More important is businesses shouldn't consider NLG a total replacement for human involvement and creativity. Companies will still need to review and tweak text or choose another text version if they don't like what was generated.

Related Article: What Is Natural Language Generation? And How Can it Facilitate Content Marketing?

Where NLG Fits in Business

Organizations with structured data from one or more sources and repetitive production patterns (based on schedule or user request) represent a typical use case for natural language generation. If you are struggling with the need to generate large volumes of text with routine frequency, this may be the solution you are looking for.

Here are several use cases by industry and/or function:

Digital Commerce

NLG works well for ecommerce marketers needing to create thousands of product descriptions for product catalogs.

"Creating custom content across a portfolio of tens of thousands of products simply isn't practical for many ecommerce websites. Using Natural Language Generation, marketers can automate the creation of certain kinds of content following the best practices of what has been most successful, saving time, resources and improving performance," said Lee Odden, CEO of TopRank Marketing.

Digital commerce is a prime example of where NLG excels. For example, combining personalization and NLG gives you the means necessary to understand if someone is shopping for themselves or for someone else and to generate the correct language in context; i.e., “Perfect for Your Birthday” versus “Perfect for Her.” It’s also practical for seasonal promotions or buzzwords that are relevant to specific times of the year, such as “Perfect for Valentine’s Day.”

Learning Opportunities

Finance and Insurance

Many news reports on stock market results are already being generated by AI-driven NLG software, and the applications in this industry are both wide and deep. For example, if you are a global banking organization and want to secure press coverage for all of your local branches, you could rely on NLG to create news releases based on the demand for mortgages in each location, generating insights into the local real estate markets and automating a previously menial task. NLG can also automate the creation of compliance reports, account statements, and any other data-driven copy helping you bypass time-intensive data crunching.


NLG is also perfect for the publishing industry, which needs to create thousands of news stories for a particular topic. Kelly Liyakasa of AdExchanger asserts that NLG allows publishers to create articles more quickly, cheaply, and potentially with fewer errors than human journalists. “It’s a critical capability for the large-scale news agency, whose content is used by other publications and journalists to develop their own localized editorial.”

According to Francesco Marconi of the Associated Press, “To give you a sense of the impact of this first project, we went from producing about 300 stories to close to 4,000 each quarter, which was a 12 times increase in content output. We also saw a reduction in error rate and were able to free up 20 percent more of reporters’ time to focus on higher-value [projects].”

Related Article: How AI Is Changing Content Marketing Today and in the Future

Additional Applications

The creation of any copy that results from data can be automated with NLG. A few examples:

  • Human resource managers who are writing and promoting hundreds of open positions within a company could automate and publish this task much faster.  
  • Travel and tourism managers can automate descriptions about locations, hotels, restaurants and more.
  • Weather and traffic reporters needing to quickly publish the weekly weather reports.

As long as there are specific data points that can be used, NLG can create the copy.

One of the more interesting applications is that NLG can help tag images for SEO purposes, an undeniably manual task. In this way, it abstracts information from pictures, combines that information with product descriptions, and creates new text to drive better search results. This can also be helpful for creating more descriptive experiences for visually challenged visitors who rely on screen readers.

Implementing NLG to boost content creation can clearly result in multiple benefits. Marketers can deliver content experiences at scale, boost efficiency and productivity, and even increase content quality by ensuring that spelling, grammar, and structure are correct and supporting the use of the corporate brand voice. But do your homework first. Don’t assume you’ll be able to flip a switch then let go of the wheel completely. There is still no replacement for good reason or for the human touch.

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